To ensure temporal coherence among video frames, the above methods usually employ OFB temporal losses to train the stylization networks. After training, the stylization networks are different from those trained without any temporal loss. The stylized frames in the two settings are different; in other words, the temporal loss may degrade the stylization quality of the individual frames. Chen et al. [1]} proposed to fix the stylization network but learn to make use of the previous frame to avoid inconsistency. Each new frame is obtained by linearly combining two frames with a learnable mask. Both frames are the outputs of the stylization network, one from the current time step and the other from the last time step but are warped to the current time step using a feature level warp function based on optical flow. This method is quite different from the existing methods and from the method proposed in this paper because it does not change the stylization network.
